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2020

SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization

Wang, Haofan, Naidu, Rakshit, Michael, Joy et al.

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Interpretation of the underlying mechanisms of Deep Convolutional Neural Networks has become an important aspect of research in the field of deep learning due to their applications in high-risk environments.

To explain these black-box architectures there have been many methods applied so the internal decisions can be analyzed and understood.
In this paper, built on the top of Score-CAM, we introduce an enhanced visual explanation in terms of visual sharpness called SS-CAM, which produces centralized localization of object features within an image through a smooth operation.
We evaluate our method on the ILSVRC 2012 Validation dataset, which outperforms Score-CAM on both faithfulness and localization tasks.

SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization · Around

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